发表机构
University of Surrey; Adobe Research（萨里大学; 奥多比研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对图像来源透明度与隐私的冲突，提出基于零知识证明的软编辑方法，通过距离证明实现隐私保护，兼容C2PA且构建与验证效率较高。
AI 中文摘要
内容来源标准如C2PA正越来越多地用于为数字图像附加经过签名的来源、编辑历史和权利记录。然而，来源透明度可能与隐私产生冲突——增强图像信任度的声明也可能泄露创作者或拍摄场景的敏感信息。我们提出针对图像来源的软编辑：一种将敏感来源声明替换为对隐藏数据选定属性的零知识证明（ZKPs）的机制。本研究聚焦于距离证明，首先展示位置声明如何通过零知识证明电路内的切比雪夫多项式近似，支持对公共参考点的邻近性证明；随后将该方法扩展到生物特征嵌入的L2距离证明，实现与相似度相关的隐私保护声明，以协助执行图像人格权；最后将相同的距离证明结构应用于感知哈希（视觉指纹），支持基于水印恢复被剥离来源元数据的反欺骗用例。我们的结果表明，对图像来源的零知识证明可提供与C2PA兼容的实用软编辑能力，其构建耗时可达秒级，验证耗时可达毫秒级。
英文摘要
Content provenance standards, such as C2PA, are increasingly used to attach signed records of origin, editing history, and rights to digital images. However, provenance transparency can conflict with privacy -- assertions that strengthen trust in an image may also reveal sensitive information about the creator or capture context. We propose soft redaction for image provenance: a mechanism that replaces sensitive provenance assertions with zero-knowledge proofs (ZKPs) of selected properties over hidden data. Our work focuses on distance proofs. We first show how location assertions can support proofs of proximity to a public reference point, using Chebyshev polynomial approximations within the ZKP proof circuit. We then extend the approach to L2 distance proofs over biometric embeddings, enabling privacy-preserving claims related to likeness to help enforce personality rights with images. Finally, we apply the same distance-proof construction to perceptual hashes (visual fingerprints), supporting an anti-spoofing use case in watermark-based recovery of stripped provenance metadata. Our results demonstrate that ZKPs over image provenance can provide practical soft-redaction capabilities, compatible with C2PA, that may be constructed in seconds and verified in milliseconds.
CommentsTo appear at ECCV 2026 workshop on Privacy Fairness Accountability and Transparency in Computer Vision (PFATCV)
